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4.5 AOD Filtering

4.5.3 Creating Photon Conversion Candidates

An example for a basic analysis step in the AOD filtering is the creation of photon candidates from conversions into e+e pairs that are then commonly used in ALICE. The individual selection criteria in the photon conversion method framework are steered via a string8. Each character in the string stands for a specific criterion and the value of the digit determines the cut value. This allows to adjust the framework to the particular conditions of, e. g., the different collision systems. In the following the applied filtering for the AOD 115 used in this analysis is discussed.

For events with a longitudinal position of the vertex|zvtx|<10 cm, the V0 candidates of the on-the-fly finder are inspected. The on-the-fly finder exhibits a higher efficiency and better momentum resolution for reconstructing photons when compared to the offline finder [277].

8For the AOD 115 filtering this string is “900177009350113211200001000000000”.

4.5 AOD Filtering 81

DCA daughters (cm)

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real cuts

toy model distributions,

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Figure 4.7: Resolution effects on cuts for signal selection (left) and background rejection (right). See text.

Both daughter tracks are required to fall into a pseudorapidity window of|η|<0.8. For larger pseudorapidities, the distribution of detector material is known to not be perfectly described in the Geant detector model. Intrinsically, this has a larger impact on the simulations of photon conversions, which result from an interaction with the material, than hadrons. A continuous effort is made to improve this by, e. g., accounting for detector support like cooling, gas and low and high voltage. For the time being, it is safer to exclude large pseudorapidities in order to ensure a good correspondance of the Monte Carlo to the data taken. A minimumpT of 50 MeV/c for both daughters is mandatory; tracks with a kink topology are rejected; and the TPC refit bit — which can be acquired in the third pass of the tracking, see Section 4.2 — is asked for. The PID capabilities of ALICE allow to suppress the vast hadronic background by a

±5σ inclusion around the expected dE/dx from a Bethe-Bloch parametrization for electrons.

Between 0.4 < p (GeV/c) < 5.0, also all particles which fall below a 3.5σ deviation in the positive direction of the expected dE/dxvalue for pions are rejected. For all remaining vertices, a Kalman package for V0 reconstruction [278] is invoked. It puts a hard constraint of zero mass and obliges the photon candidate to stem from the primary vertex. The Kalman package incorporates the restrictions into aχ2 per number of degrees of freedom measure. Candidates which exceed the value of 100 in this observable are discarded. The zero mass of the photon

furthermore implies that the two helix approximations of the decay daughters ideally intersect in one point in the transverse (R, ϕ) plane with the daughter momenta parallelly aligned. This allows to recalculate the decay vertex position as described in [279] — improving the secondary vertex resolution [280].

A powerful tool for studying V0 particles is the Armenteros-Podolanski plot [281]. Fig. 4.8 (left) shows a typical example in ALICE. The Armenteros-Podolanski plot shows the momentum of one decay daughter transverse to the V0 flight direction pArm.T vs. the asymmetryα of the decay

pArm.T =|~p+|sin(ϑ+) =|~p|sin(ϑ), (4.6) α= p+k −pk

p+k +pk , (4.7)

where ~p+ (~p) is the three-momentum of the positive (negative) daugther, ϑ+) is the angle of the momentum of the positive (negative) daughter and the momentum of the V0, and p+k and pk are the momenta of the daughters parallel to the flight direction of the V0. The strength of an Armenteros-Podolanski plot is that the different species can be spotted immediately and picking specific particles is straightforward: a selection of pArm.T <0.1 GeV/c allows to reject non-photons. The cut is especially effective against a contamination from K0s with their relatively high momentum released in the decay of 206 MeV/c. This is demonstrated in Fig. 4.8 (right) where it can be seen that the distribution of the Armenteros transverse momentum pArm.T is very peaked at the maximal possible value and rejecting V0 particles with a pArm.T above a certain threshold effectively reduces the contamination from V0 with a large momentum release during their decay like K0s.

Fig. 4.9 (left) shows the transverse spatial distribution of photon decay vertices in pp collisions from 2010 together with a Monte Carlo simulation. It can be seen from the figure, that the number of conversions approaches zero for R → 180 cm. Removing vertices with R > 180 cm ensures a good leverage arm of the TPC for the decay leptons. Also requiring a minimum R of 5 cm further reduces the contamination from K0s (cτ = 2.68 cm) and Λ (cτ = 7.68 cm) and combinatorial background while — as can be seen from Fig. 4.9 (left) —

rejecting only a small fraction of photons.

With the improved constraints, the spatial vertex position is (re-)checked to fall into a volume analogous to the aforementioned pseudorapidity window. A minimum of 60% of reconstructed over findable clusters for both daughters starting from the photon conversion vertex radially outwards certifies a good resolution and a cut on the Ψpair further selects conversions. Ψpair describes the angle between the plane formed by the two daughter tracks and the magnetic field. As no momentum is released in the photon conversion, the decay plane is always normal to the magnetic field — in contrast to other two-particle sources — as visualized in Fig. 4.9.

4.5 AOD Filtering 83

) c (MeV/

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Figure 4.8: Left: A typical Armenteros-Podolanski plot of V0 particles in ALICE for Pb-Pb collisions. Taken from [282]. Right: Probability distribution of the Armenteros transverse momentum pArm.T for a K0s decay into two charged pions in black. One sees that the pArm.T distribution peaks at the maximal possiblepArm.T of 206 MeV/c. Apart from the position of the maximum, the shape of the distribution is fully given by the projection of the three-dimensional shell of the daughter momenta in the decay rest frame onto pArm.T . Cumulative probability distribution in red. A selection on pArm.T <100 MeV/c rejects about 90% of all K0s.

Finally, only candidates whose pointing angle (see Section 4.3) fulfills the condition cos(θ)> π are written asAliAODConversionPhotonto the delta AOD fileAliAODGammaConversion.root.

The stored photon candidates have a decent purity and good quality, so that a basic analysis can immediately follow without further restrictions on the sample; the imposed criteria are loose enough to accomodate the needs of any general photon analysis.

R (cm)

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chN

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Data

MC converison candidates MC true converison

Dalitz π0 MC true

Dalitz MC true η MC true Combinatorics Beam Pipe SPD LayerstSDD 1 Layer + Support StructuresndSDD 2 LayerstSSD 1 LayerndSSD 2 TPC Inner Containment Vessel TPC Inner Field Cage Vessel TPC Gas

ALICE Performance = 7 TeV s pp @

May 2011 10th Phojet LHC10d

Figure 4.9: Left: Transverse radius R distribution of photon decay vertices in data compared to a simulation with the Monte Carlo generator PHOJET [283, 284].

The Monte Carlo allows for the separation of the different photon sources. Right:

Visualization of the angle Ψpair which is formed by the plane perpendicular to the magnetic field and the one formed by the two daughter tracks. Taken from [285].

Chapter 5

pΛ Data Analysis: Event and Single-Particle Selection

5.1 Event Characteristics

Within this thesis, the second reconstruction pass of the 2011 Pb-Pb data, denoted by ALICE internally as ‘LHC11h’, are analyzed. Unless explicitly stated otherwise, all distributions are obtained with the AOD 115 dataset.

The Run Condition Table (RCT)1 easily lets the user select proper runs by choosing a good global quality.2 Restricting the analysis to events with the longitudinal position of the primary vertex |zvtx|< 10 cm ensures a uniform acceptance in pseudorapidity. Events are grouped according to their centrality; the standard estimator V0M (see Section 4.1) is used for this. The common centrality framework also flags events which show an abnormal correlation between different centrality estimators. Although the centrality framework was asked to reject outliers in, e. g., the correlation of the TRK and the V0M estimator by accepting only events with the quality flag of the centrality task equal to zero3, it appears from Fig. 4.1 (left) that this tagging should be re-checked within the centrality framework. Nevertheless, the agreement between different centrality estimators is good, as previously stated in Section 4.1.

1Seehttp://alimonitor.cern.ch/configuration/.

2In the RCT, a good quality is represented by the value one.

3Seehttps://twiki.cern.ch/twiki/bin/viewauth/ALICE/CentStudies.

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